Real Vehicular Ad Hoc Networks Research Articles

Hyper-Flophet: A neural Prophet-based model for traffic flow forecasting in transportation systems

Nowadays, an accurate and reliable traffic forecast is meaningful in making the right decisions for traffic management systems in vehicular environments. Nevertheless, traffic flow prediction is a significant challenge in Vehicular Ad Hoc Networks (VANETs) that has taken much attention. Therefore, in this paper, we propose a hybrid traffic prediction model based on Prophet model and Long Short-Term Memory neural network (LSTM), called Hyper-Flophet, to predict next traffic flow. Hyper-Flophet model adopts the traditional neural prophet model but with major parameter tuning. First, we propose an efficient algorithm for predicting the traffic flow trend then, we develop an interactive LSTM (I-LSTM) model for auto-regression components. After that, we implement a new future regressor component called network mobility and finally, we enhance the event and holiday component by introducing exponential growth term. Through simulations with real VANET data, we show that the proposed hybrid approach can achieve superior forecasting performance over other models.

Incremental Online Machine Learning for Detecting Malicious Nodes in Vehicular Communications Using Real-Time Monitoring

Detecting malicious activities in Vehicular Ad hoc Networks (VANETs) is an important research field as it can prevent serious damage within the network and enhance security and privacy. In this regard, a number of approaches based on machine learning (ML) algorithms have been proposed. However, they encounter several challenges due to data being constantly generated over time; this can impact the performance of models trained on fixed datasets as well as cause the need for real-time data analysis to obtain timely responses to potential threats in the network. Therefore, it is crucial for machine learning models to learn and improve their predictions or decisions in real time as new data become available. In this paper, we propose a new approach for attack detection in VANETs based on incremental online machine learning. This approach uses data collected from the monitoring of the VANET nodes’ behavior in real time and trains an online model using incremental online learning algorithms. More specifically, this research addresses the detection of black hole attacks that pose a significant threat to the Ad hoc On Demand Distance Vector (AODV) routing protocol. The data used for attack detection are gathered from simulating realistic VANET scenarios using the well-known simulators Simulation of Urban Mobility (SUMO) and Network Simulator (NS-3). Further, key features which are relevant in capturing the behavior of VANET nodes under black hole attack are monitored over time. The performance of two online incremental classifiers, Adaptive Random Forest (ARF) and K-Nearest Neighbors (KNN), are assessed in terms of Accuracy, Recall, Precision, and F1-score metrics, as well as training and testing time. The results show that ARF can be successfully applied to classify and detect black hole nodes in VANETs. ARF outperformed KNN in all performance measures but required more time to train and test compared to KNN. Our findings indicate that incremental online learning, which enables continuous and real-time learning, can be a potential method for identifying attacks in VANETs.

Enhancing OLSR protocol in VANETs with multi-objective particle swarm optimization

The Optimized Link State Routing Protocol (OLSR) is envisioned as one of the key components to the operation of future vehicular ad hoc networks (VANETs). Its performance is however fairly constrained in the vehicular communication environment due to the limited coverage of wireless transmission and high-speed movement of nodes. In this brief, we propose a multi-objective particle swarm optimization (MOPSO) framework to enhance the performance of OLSR in VANETs. Specifically, we formulate a multi-objective optimization problem (MOP) considering not only the quality of service (QoS), including throughput, delay and packet loss rate, but also the cost of service, i.e., routing load. We solve this MOP with MOPSO and obtain the Pareto front corresponding to the optimal equilibrium of performance and cost. In the experiment, our optimization framework is used in a general setting of VANETs to obtain the optimal parameters of OLSR, i.e., Hello and TC intervals, and these parameters are further validated in realistic VANET scenarios. Our optimization framework can be naturally extend to other dynamic routing protocols in networks.

SATMAC: Self-Adaptive TDMA-Based MAC Protocol for VANETs

Rapid development and deployment of vehicular ad-hoc networks (VANETs) require an efficient and scalable media access control (MAC) protocol to support high-priority safety applications and infotainment requirements. This paper proposes SATMAC, a self-adaptive time division multiple access (TDMA)-based MAC protocol for VANETs. In order to improve the stability of the time slot scheduling in VANETs, a slot status updating strategy is carefully designed, which utilizes accurate information of the two-hop neighbors and the rough information of the three-hop neighbors to detect the potential packet collisions and avoids potential collisions by adjusting the occupied time slot. Besides, an adaptive frame length (the number of time slots contained in a frame) approach is proposed on the basis of the slot adjustment to support various densities of vehicles, where the frame length between neighbors can be inconsistent. We conduct theoretical analysis and extensive simulations in a realistic VANET environment to evaluate SATMAC. Simulation results show that compared with IEEE 802.11p and LTE-V2X PC5 Mode 4, SATMAC significantly improves PDR of beacons over 60%. Moreover, our SATMAC design is further implemented and validated on our FPGA-based testbed.

A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs

Routing security attacks in Vehicular Ad hoc Networks (VANETs) represent a challenging issue that may dramatically decrease the network performances and even cause hazardous damage in both lives and equipment. This study proposes a new approach named Multivariate Statistical Detection Scheme (MVSDS), capable of detecting routing security attacks in VANETs based on statistical techniques, namely the multivariate normality tests (MVN). Our detection approach consists of four main stages: first, we construct the input data by monitoring the network traffic in real time based on multiple metrics such as throughput, dropped packets ratio, and overhead traffic ratio. Secondly, we normalize the collected data by applying three different rescaling techniques, namely the Z-Score Normalization (ZSN), the Min-Max Normalization (MMN), and the Normalization by Decimal Scaling (NDS). The resulting data are modeled by a multivariate dataset sampled at different times used as an input by the detection step. The next step allows separating legitimate behavior from malicious one by continuously verifying the conformity of the dataset to the multivariate normality assumption by applying the Rao–Ali test combined with the Ryan–Joiner test. At the end of this step, the Ryan–Joiner correlation coefficient (R–J) is computed at various time windows. The measurement of this coefficient will allow identifying an attacker’s presence whenever this coefficient falls below a threshold corresponding to the normal critical values. Realistic VANET scenarios are simulated using SUMO (Simulation of Urban Mobility) and NS-3 (network simulator). Our approach implemented in the Matlab environment offers a real time detection scheme that can identify anomalous behavior relying on multivariate data. The proposed scheme is validated in different scenarios under routing attacks, mainly the black hole attack. As far as we know, our proposed approach unprecedentedly employed multivariate normality tests to attack detection in VANETs. It can further be applied to any VANET routing protocol without making any additional changes in the routing algorithm.

Distributed Channel and Power Level Selection in VANET Based on SINR using Game Model

This paper proposes a scheme of channel selection and transmission power adjustment in Vehicular Ad hoc Network (VANET) using game theoretic approach. The paradigm of VANET enables groups of vehicles to establish a mesh-like communication network. However, the mobility of vehicle, highly dynamic network environment, and the shared-spectrum concept used in VANET pose some challenges such as interference that can decrease the quality of signal. Channel selection and transmit power adjustment are aimed to obtain the higher signal to interference and noise ratio (SINR). In this paper, game theory is implemented to model the channel and power level selection in VANET. Each vehicle represents the player and the combination of channel and power level represents the strategy used by the player to obtain the utility i.e. the SINR. Strategy selection is arranged distributively to each player using Regret Matching Learning (RML) algorithm. Each vehicle evaluates current utility obtained by selecting a strategy to define the probability of that strategy to be selected in the next time. However, RML has a shortcoming for using assumption that hard to be implemented in real VANET environment. Therefore modification of RML devised for this application is also proposed. The simulation model of channel and power level selection is build to evaluate the performance of the proposed scheme. The results of simulation display the improvement of VANET performance in term of SINR and throughput from the proposed scheme.

Advanced Greedy Hybrid Bio-Inspired Routing Protocol to Improve IoV

New vehicles are now expected to be involved in the rapid development of Intelligent Transport Systems (ITS). Vehicular Ad hoc NETwork (VANET) is the basic equipment used for the production of ITSs with a rapid and dynamic network topology. The increasing number of connected vehicles and the need for real-time data processing has created a growing demand for turning real VANETs into an automotive Internet of Vehicle (IoV) for achieving a goal of an effective and smart future transportation system. In this paper, an Advanced Greedy Hybrid Bio-Inspired (AGHBI) routing protocol with a greedy forwarding system is proposed to improve the performance of IoV, where a modified hybrid routing scheme with the help of a bee colony optimization is used to select the highest quality of service route and maintain the path with minimum overflow. Simulation results confirm that the proposed protocol can cope well with both Vehicle to Vehicle (V2V), and Vehicle to Infrastructure (V2I) environments and has a great impact on improving the packet delivery ratio, and the delay, while attaining acceptable overhead and hops count among all vehicles.

EmuCD: An Emulator for Content Dissemination Protocols in Vehicular Networks

The development of protocols for mobile networks, especially for vehicular ad-hoc networks (VANETs), presents great challenges in terms of testing in real conditions. Using a production network for testing communication protocols may not be feasible, and the use of small networks does not meet the requirements for mobility and scale found in real networks. The alternative is to use simulators and emulators, but vehicular network simulators do not meet all the requirements for effective testing. Aspects closely linked to the behaviour of the network nodes (mobility, radio communication capabilities, etc.) are particularly important in mobile networks, where a delay tolerance capability is desired. This paper proposes a distributed emulator, EmuCD, where each network node is built in a container that consumes a data trace that defines the node’s mobility and connectivity in a real network (but also allowing the use of data from simulated networks). The emulated nodes interact directly with the container’s operating system, updating the network conditions at each step of the emulation. In this way, our emulator allows the development and testing of protocols, without any relation to the emulator, whose code is directly portable to any hardware without requiring changes or customizations. Using the facilities of our emulator, we tested InterPlanetary File System (IPFS), Sprinkler and BitTorrent content dissemination protocols with real mobility and connectivity data from a real vehicular network. The tests with a real VANET and with the emulator have shown that, under similar conditions, EmuCD performs closely to the real VANET, only lacking in the finer details that are extremely hard to emulate, such as varying loads in the hardware.

Enhancing Location Privacy for Vehicular Ad Hoc Networks

Communication messages in Vehicular Ad-hoc Networks (VANETs) can be used to track movement of vehicles. In this paper, we address the problem of movement tracking and enhance location privacy without affecting security and safety of vehicles. By considering unique characteristics of VANETs, we firstly propose a synchronized pseudonym changing protocol based on the concept of forming groups among neighboring vehicles. Secondly, we analytically evaluate the anonymity and unlinkability of the proposed protocol. Finally, we do a series of simulations to evaluate the performance of our protocol in real VANET environments such as Manhattan and Urban. Simulation results show that our protocol is feasible and produces excellent performances. The main advantages of our protocol compared with the existing approaches include: 1) it makes larger anonymity set and higher entropy; 2) it reduces the tracking probability; 3) it can be used in both safety and non-safety communications; and 4) Vehicles need not suspend regular communication for changing pseudonyms.

A Heterogeneous Network Modeling Method Based on Public Goods Game Theory to Explore Cooperative Behavior in VANETs.

Cooperative vehicular networking has been widely studied in recent years. Existing evolution game theoretic approaches to study cooperative behavior in Vehicular Ad hoc Network (VANET) are mainly based on the assumption that VANET is constructed as a homogeneous network. This modeling method only extracts part attributes of vehicles and does not distinguish the differences between strategy and attribute. In this paper, we focus on the heterogeneous network model based on the public goods game theory for VANET. Then we propose a Dynamic Altruism Public Goods Game (DAPGG) model consisting of rational nodes, altruistic nodes, and zealots to more realistically characterize the real VANET. Rational nodes only care about their own benefits, altruistic nodes comprehensively consider the payoffs in the neighborhood, while zealots insist on behaving cooperatively. Finally, we explore the impacts of these attributes on the evolution of cooperation under different network conditions. The simulation results show that only adding altruistic nodes can effectively improve the proportion of cooperators, but it may cause conflicts between individual benefits and neighborhood benefits. Altruistic nodes together with zealots can better improve the proportion of cooperators, even if the network conditions are not suitable for the spread of cooperative behavior.

Vehicle density and signal to noise ratio based broadcast backoff algorithm for VANETs

In a realistic vehicular ad-hoc networks (VANETs), a beacon frame loss is usually caused by collision or channel noise. In this paper, we propose an effective scheme for the periodic broadcast on the control channel in VANETs. Instead of the fixed contention window, in our scheme, the contention window is dynamically adjusted. For a certain vehicle density, the best contention window size is obtained by maximizing the transmission success rate in a unit time slot. On this basis, the channel quality is evaluated according to the signal to noise ratio (SNR), and the same frame will be transmitted twice under certain conditions. Simulation results show that the proposed vehicle density and SNR based broadcast algorithm outperforms the traditional broadcast methods.

A Public Goods Game Theory-Based Approach to Cooperation in VANETs Under a High Vehicle Density Condition

There are high demands for cooperation among vehicle nodes to disseminate data in vehicular ad hoc networks (VANETs). Game theory approaches have been applied to analyzing and regulating the behavior of nodes in complex networks. However, game theory-based cooperation in VANETs still poses a research challenge due to continuous changes in the network topology. In this paper, we focus on the issue of the common behavior of cooperation in group vehicular interactions. We first present a dynamic member public goods game (PGG) model for real VANETs in which games are carried out within each group. Then, a dynamic grouping PGG (DGPGG) model is proposed to model VANETs with high-node density. In addition, a greedy neighbor selection strategy is proposed to replace the typical random strategy. Finally, the individual reputation of a vehicle is integrated into the DGPGG model. The simulation results show that the DGPGG model can greatly increase the proportion of cooperative nodes in high-density VANETs no matter whether the network is static or dynamic. When the network topology changes quickly, the greedy neighbor selection strategy can promote cooperation between nodes more effectively than the random strategy.

Providing location privacy for online services in vehicular ad hoc networks

Communications through road side units in vehicular ad hoc networks (VANETs) can be used to track the location of vehicles, which makes serious threat on users' privacy. In this paper, we address the problem of location tracking for online service access and the privacy enhancement in VANETs. Firstly, by considering the unique characteristics of VANETs, we propose an anonymous online service access (AOSA) protocol. Secondly, we analytically evaluate the anonymity and the unlinkability of the proposed protocol. Finally, a series of simulation studies are conducted to evaluate the performance of our protocol in the real VANET environments such as Manhattan and urban scenarios. According to analytical evaluation and simulations, our protocol provides higher level of anonymity and location privacy by providing larger anonymity set and smaller tracking probability for online service access applications. Simulation results further show that our protocol is feasible and produce better performance in real VANET environments by producing higher success ratio and smaller delay.

The impact of ECDSA in a VANET routing service: Insights from real data traces

Both the WAVE IEEE 1609.2 standard in USA and the ETSI ITS security standards in Europe rely on the Elliptic Curve Digital Signature Algorithm (ECDSA) to authenticate messages exchanged among vehicles. Although being faster than other equivalent algorithms, the ECDSA computational cost nevertheless affects the message validation throughput. Even worse, the number of messages that a device has to authenticate may easily exhaust its computational limits.In this article, we evaluated the impact caused by ECDSA authentication of messages of the multi-hop routing control plane used in a real Vehicular Ad Hoc Network (VANET). Such control plane uses periodic vicinity updates to keep accurate, distributed routing paths, and ECDSA-based validation delays may force to discard many of such updates.To perform the evaluation of the impact imposed by ECDSA we considered the multiple curve parameters associated to WAVE and ETSI ITS, their implementation by different cryptographic libraries and their performance in distinct hardware. We took as reference for traffic to be authenticated with ECDSA a day-long set of messages of a VANET routing control plane. These messages were inferred from connectivity status samples from all mobile nodes of a real VANET. Emulation results with those messages show that, without high-end computing devices, ECDSA authentication would have a substantial negative impact in the routing service of the tested VANET.

Localization Prediction in Vehicular Ad Hoc Networks

Localization systems play a major role in many applications for vehicular ad hoc networks (VANETs). One of the most interesting problems to be solved in vehicular networks is how to provide anywhere and anytime highly accurate and reliable localization information. Unique characteristics of VANETs such as mobility constraints, driver’s behavior, and the high-speed displacement nature of vehicles cause rapid and constant changes in network topology, leading to dissemination of outdated localization information. To circumvent this problem, an alternative is the use of predicted future locations of vehicles. The main idea of this approach is to use the localization prediction as an extension of a data fusion localization system. In such an approach, a future position of a vehicle is predicted for a given future time and used to take advantage of a future time-space window of a vectorial trajectory rather than a static localization point. In this paper, we discuss this subject by studying and analyzing the use of localization prediction as natural way to improve VANET applications. We survey proposed approaches for localization, target tracking, and time series prediction techniques that can be used to estimate the future position of a vehicle. We also highlight their advantages and disadvantages through an analytical discussion visualizing its potential application scenarios in VANETs. We present a set of experiments that show the results of such techniques when applied to a realistic VANET scenario. 1 1 This work extends the original contribution proposed by Balico et al. [1] . The current version includes several new contributions: a more complete description of the localization prediction methods and its application to VANETs; additional comparisons with other algorithms; and an analytical discussion of the localization prediction algorithms.

Distributed Channel and Power Level Selection in VANET Based on SINR using Game Model

This paper proposes a scheme of channel selection and transmission power adjustment in Vehicular Ad hoc Network (VANET) using game theoretic approach. The paradigm of VANET enables groups of vehicles to establish a mesh-like communication network. However, the mobility of vehicle, highly dynamic network environment, and the shared-spectrum concept used in VANET pose some challenges such as interference that can decrease the quality of signal. Channel selection and transmit power adjustment are aimed to obtain the higher signal to interference and noise ratio (SINR). In this paper, game theory is implemented to model the channel and power level selection in VANET. Each vehicle represents the player and the combination of channel and power level represents the strategy used by the player to obtain the utility i.e. the SINR. Strategy selection is arranged distributively to each player using Regret Matching Learning (RML) algorithm. Each vehicle evaluates current utility obtained by selecting a strategy to define the probability of that strategy to be selected in the next time. However, RML has a shortcoming for using assumption that hard to be implemented in real VANET environment. Therefore modification of RML devised for this application is also proposed. The simulation model of channel and power level selection is build to evaluate the performance of the proposed scheme. The results of simulation display the improvement of VANET performance in term of SINR and throughput from the proposed scheme.

Taguchi Method to Measure the Impact of GPSR Routing Protocol Parameters in VANET Performances

Background/Objectives: Routing protocols play an important role in the performances of Vehicular Ad Hoc networks (VANET). Routing protocols procedures are controlled with a set of parameters from being dragged to undesired situations such as flooding the network with unnecessary overhead. These parameters have a direct impact on the efficiency of a routing protocol and the overall network performances. The impacts of these parameters on the network performances are less considered in the literature. Methods: This paper proposed the Taguchi delta analysis to measure the impact of Greedy Perimeter Stateless Routing (GPSR) protocols parameters on the performances of VANET. Then, the obtained results are investigated with iterative method to validate the Taguchi observations. Further, an offline optimization of the GPSR parameters is evaluated by performing Taguchi SNR method for fine-tuning the GPSR routing parameters. Application/ Improvements: GPSR with Taguchi tuned is compared with GPSR configuration in a realistic VANET scenario. The obtained results showed that, GPSR with Taguchi configuration outperformed the GPSR in terms of delay and PDR on the simulated scenarios. Keywords: GPSR, Optimization, Routing Parameters, Taguchi Method, VANET

Fuzzy Logic-based Intelligent Scheme for Enhancing QoS of Vertical Handover Decision in Vehicular Ad-hoc Networks

The design of next generation networks in various technologies under the “Anywhere, Anytime” paradigm offers seamless connectivity across different coverage. A conventional algorithm such as RSSThreshold algorithm, that only uses the received strength signal (RSS) as a metric, will decrease handover performance regarding handover latency, delay, packet loss, and handover failure probability. Moreover, the RSS-based algorithm is only suitable for horizontal handover decision to examine the quality of service (QoS) compared to the vertical handover decision in advanced technologies. In the next generation network, vertical handover can be started based on the user’s convenience or choice rather than connectivity reasons. This study proposes a vertical handover decision algorithm that uses a Fuzzy Logic (FL) algorithm, to increase QoS performance in heterogeneous vehicular ad-hoc networks (VANET). The study uses network simulator 2.29 (NS 2.29) along with the mobility traffic network and generator to implement simulation scenarios and topologies. This helps the simulation to achieve a realistic VANET mobility scenario. The required analysis on the performance of QoS in the vertical handover can thus be conducted. The proposed Fuzzy Logic algorithm shows improvement over the conventional algorithm (RSSThreshold) in the average percentage of handover QoS whereby it achieves 20%, 21% and 13% improvement on handover latency, delay, and packet loss respectively. This is achieved through triggering a process in layer two and three that enhances the handover performance.

Coherent, automatic address resolution for vehicular ad hoc networks

The interest in vehicular communications has increased notably. In this paper, the use of the address resolution (AR) procedures is studied for vehicular ad hoc networks (VANETs).We analyse the poor performance of AR transactions in such networks and we present a new proposal called coherent, automatic address resolution (CAAR). Our approach inhibits the use of AR transactions and instead increases the usefulness of routing signalling to automatically match the IP and MAC addresses. Through extensive simulations in realistic VANET scenarios using the Estinet simulator, we compare our proposal CAAR to classical AR and to another of our proposals that enhances AR for mobile wireless networks, called AR+. In addition, we present a performance evaluation of the behaviour of CAAR, AR and AR+ with unicast traffic of a reporting service for VANETs. Results show that CAAR outperforms the other two solutions in terms of packet losses and furthermore, it does not introduce additional overhead.

APDM: An adaptive multi-priority distributed multichannel MAC protocol for vehicular ad hoc networks in unsaturated conditions

Vehicular ad hoc networks (VANETs) employ multichannel to provide a variety of safety and non-safety applications. Safety applications require reliable and timely transmission, while non-safety applications need high network throughput. IEEE 802.11p and IEEE 1609.4 protocol divide the bandwidth into seven channels. One control channel (CCH) is to serve safety applications and the other six service channels (SCHs) to serve non-safety applications. The IEEE 1609.4 protocol specifies an alternating scheme to allow vehicles to switch between two types of applications. However, the IEEE 1609.4 multichannel media access control (MAC) protocol has limitations on its capability of supporting either delay- or throughput-sensitive applications. In this paper, we propose an adaptive multi-priority distributed multichannel (APDM) MAC protocol for VANETs. Considering that in realistic VANETs, the queue of MAC layer is far from saturated. We assume that generated packets with different priorities arrive at the MAC layer in a Poisson manner. A Markov analytical model is conducted to optimize the packet transmission probabilities and adjust the ratio between CCH interval and SCH interval dynamically according to the real-time traffic in a distributed way. An M/M/1 queue model is then adopted to analyze the time performance. Extensive simulation results show that the APDM MAC protocol can ensure prioritized transmission of safety packets, reduce the transmission delay of packets and enhance the unsaturated and saturated throughput of SCHs.